Update app.py

app.py

@@ -88,22 +88,27 @@ def score_news(parsed_news_df):
 
 
 def plot_hourly_sentiment(parsed_and_scored_news, ticker):
-   
+    # Ensure that only numeric columns are resampled
+    numeric_cols = parsed_and_scored_news.select_dtypes(include=['float64', 'int64'])
+    
     # Group by date and ticker columns from scored_news and calculate the mean
-    mean_scores = parsed_and_scored_news.resample('H').mean()
+    mean_scores = numeric_cols.resample('h').mean()
 
-    # Plot a bar chart with plotly 
-    fig = px.bar(mean_scores, x=mean_scores.index, y='sentiment_score', title = ticker + ' Hourly Sentiment Scores')
-    return fig # instead of using fig.show(), we return fig and turn it into a graphjson object for displaying in web page later
+    # Plot a bar chart with Plotly 
+    fig = px.bar(mean_scores, x=mean_scores.index, y='sentiment_score', title=ticker + ' Hourly Sentiment Scores')
+    return fig  # Return the figure to display in the Streamlit app
 
 def plot_daily_sentiment(parsed_and_scored_news, ticker):
-   
+    # Ensure that only numeric columns are resampled
+    numeric_cols = parsed_and_scored_news.select_dtypes(include=['float64', 'int64'])
+
     # Group by date and ticker columns from scored_news and calculate the mean
-    mean_scores = parsed_and_scored_news.resample('D').mean()
+    mean_scores = numeric_cols.resample('D').mean()
+
+    # Plot a bar chart with Plotly
+    fig = px.bar(mean_scores, x=mean_scores.index, y='sentiment_score', title=ticker + ' Daily Sentiment Scores')
+    return fig  # Return the figure to display in the Streamlit app
 
-    # Plot a bar chart with plotly
-    fig = px.bar(mean_scores, x=mean_scores.index, y='sentiment_score', title = ticker + ' Daily Sentiment Scores')
-    return fig # instead of using fig.show(), we return fig and turn it into a graphjson object for displaying in web page later
 
 # for extracting data from finviz
 finviz_url = 'https://finviz.com/quote.ashx?t='